• Define the end-to-end technical architecture for assessment capabilities —challenge delivery, scoring infrastructure, reporting pipelines, and enterprise integrations—with full accountability for outcomes. • Architect containerized CTF-style challenge environments with dynamic flag generation, automated attack simulation, participant isolation, and anti-cheat mechanisms integrated with the ranges.io platform. • Integrate the reporting layer that turns participant performance data into employer-ready insights: role suitability scores, skill gap analysis, ranked candidate views, and personalized SANS course recommendations. • Partner with Product, UX, Cybersecurity SMEs, and executive leadership to translate strategy into roadmaps; present technical decisions to VP/C-level audiences; and drive alignment across engineering, security, and operations. • Conduct architecture and code reviews; recruit top talent; and foster a culture of engineering excellence across the ASAP organization. • Own platform reliability, security posture, and DevOps maturity—CI/CD, infrastructure as code, containerization (Docker/Kubernetes), and support for on-demand, remote, hybrid, and in-person delivery modes. • Foster a culture of engineering excellence and serve as the technical escalation point for complex problems across teams. • Perform other related duties as assigned.

• Build and maintain iOS applications using Swift and SwiftUI. • Integrate AI-powered features through backend APIs. • Design UX patterns for AI interactions, including loading states, streaming responses, retries, and fallbacks. • Optimize performance, memory usage, and battery efficiency. • Capture user signals and feedback to support AI evaluation and iteration. • Collaborate closely with backend and ML engineers on API design and system behavior. • Maintain high App Store quality, stability, and production reliability.

• Own the application engineering strategy and execution across backend, mobile, and desktop. • Lead and grow a small, senior applications team, including backend, mobile, and desktop engineers. • Ensure production-grade reliability: observability, monitoring, retries, fallbacks, and failure handling. • Set the architectural direction for AI-powered product workflows, APIs, and client integrations. • Ensure AI capabilities are integrated into the product with clear abstractions, predictable behavior, and graceful failure modes. • Partner closely with Machine Learning leadership to translate model capability into shippable product features. • Make high-impact decisions across latency, cost, reliability, security, and user experience. • Establish a strong execution culture through code reviews, design reviews, and hands-on technical leadership. • Ensure production readiness: observability, monitoring, retries, fallbacks, privacy, and cost controls. • Balance speed and discipline – shipping quickly without compromising long-term system quality.

• Build end-to-end product features across frontend, backend, and AI integrations • Design agent workflows that handle planning, tool use, failure, and recovery across multiple steps. • Integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions • Design real-time AI interactions with streaming, partial results, and tight latency constraints • Improve system reliability, observability, and fallback mechanisms • Collaborate closely with ML, backend, and product teams to ship features end-to-end • Continuously iterate based on real usage and failure modes